
Address the problem of spatial precision loss by obtaining hourglass models with better generalization by focusing on segmented objects' contours.

Improve the SS results in hourglass models by using complementary information from contour-based tasks, and thus induce a clustering behavior in the latent space.

Extensively evaluate common hourglass models, namely SegNet [1]} and UNet [2]}, on Cityscapes [3]}, and CamVid [4]} datasets.
Moreover, we show that the use of complementary tasks improves the state-of-the-art in hourglass (encoder-decoder) models.

